## The Kuhn-Tucker theorem: a first look

In its simplest form, this theorem addresses optimization with an inequality constraint: maximize the objective function subject to the inequality constraint .

If the solution satisfies

(1)

the constraint is called **efficient** (**binding**), otherwise it is called **slack **(or **nonbinding**). Here we answer the question: when an efficient constraint is efficient? More precisely, what can be said if the maximum is attained at a point such that (1) is true, as opposed to ? Note that when the maximum in fact satisfies (1), we can use the Lagrangian if the implicit function existence condition holds:

(2)

## Constrained set as a union of level sets

Consider the curve (level set) , . Obviously, the **constrained set** is a union of these level sets. We need to compare the values of the objective function on with those on for .

## Applying the Lagrangian multiplier interpretation

Assuming (2), let be the Lagrangian multiplier for the equality constrained problem: maximize subject to Writing as with we can apply the equation

(3)

where is the maximized value on the curve . To simplify the notation, the maximizing point on the curve is denoted and the maximum value In terms of the original variable equation (3) becomes

This equation explains everything. If the value is larger than or equal to , , then the derivative (1) is nonpositive, which translates to nonnegativity of

**Conclusion**. If a constraint is efficient, then the Lagrange multiplier is nonnegative.

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